Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	129,101
2	South Dakota	124,182
3	Rhode Island	113,635
4	Utah	111,848
5	Arizona	108,905
6	Tennessee	108,041
7	Wisconsin	103,853
8	Iowa	103,779
9	Oklahoma	103,463
10	Arkansas	103,069
11	Nebraska	100,933
12	Kansas	98,857
13	Alabama	97,399
14	Indiana	96,280
15	Mississippi	95,648
16	Idaho	93,463
17	Nevada	93,023
18	South Carolina	92,506
19	Wyoming	91,538
20	Illinois	91,430
21	Montana	90,699
22	Louisiana	89,789
23	Texas	87,736
24	Georgia	87,690
25	California	87,575
26	Kentucky	87,040
27	New Mexico	85,521
28	Florida	84,124
29	Delaware	84,080
30	Minnesota	83,493
31	New Jersey	82,899
32	Missouri	82,550
33	Massachusetts	79,999
34	Ohio	79,684
35	New York	77,843
36	North Carolina	77,724
37	Alaska	76,345
38	Connecticut	74,748
39	Colorado	71,542
40	West Virginia	70,541
41	Pennsylvania	69,528
42	Virginia	63,384
43	Michigan	62,697
44	Maryland	60,877
45	District of Columbia	54,598
46	New Hampshire	51,196
47	Washington	43,227
48	Puerto Rico	40,783
49	Oregon	35,346
50	Maine	31,288
51	Vermont	21,232
52	Hawaii	18,780

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	South Carolina	510
2	Kentucky	456
3	Texas	439
4	New Jersey	435
5	Arizona	433
6	New York	426
7	Arkansas	405
8	Rhode Island	401
9	Virginia	398
10	North Carolina	392
11	Oklahoma	371
12	Utah	359
13	Florida	358
14	Georgia	353
15	Delaware	323
16	Louisiana	317
17	Tennessee	311
18	Pennsylvania	297
19	Massachusetts	294
20	Montana	294
21	Alabama	287
22	New Hampshire	272
23	Ohio	265
24	Mississippi	263
25	Connecticut	258
26	California	250
27	West Virginia	244
28	Iowa	233
29	New Mexico	231
30	Kansas	226
31	Idaho	223
32	South Dakota	217
33	Alaska	216
34	Indiana	212
35	Nebraska	206
36	Illinois	203
37	Nevada	198
38	Wisconsin	188
39	District of Columbia	187
40	Colorado	185
41	Maryland	182
42	Missouri	168
43	Maine	158
44	Oregon	131
45	Minnesota	125
46	Michigan	122
47	Wyoming	112
48	Washington	111
49	Vermont	108
50	North Dakota	75
51	Puerto Rico	49
52	Hawaii	38

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,513
2	New York	2,312
3	Massachusetts	2,215
4	Mississippi	2,147
5	Rhode Island	2,146
6	South Dakota	2,067
7	Connecticut	2,062
8	Arizona	2,018
9	Louisiana	1,987
10	North Dakota	1,915
11	Alabama	1,839
12	Pennsylvania	1,789
13	Indiana	1,786
14	Illinois	1,734
15	Arkansas	1,722
16	New Mexico	1,660
17	Iowa	1,646
18	Michigan	1,603
19	Tennessee	1,569
20	South Carolina	1,522
21	Nevada	1,506
22	Kansas	1,477
23	Georgia	1,417
24	Texas	1,402
25	District of Columbia	1,378
26	Florida	1,321
27	Delaware	1,296
28	Missouri	1,270
29	Montana	1,236
30	Maryland	1,235
31	West Virginia	1,220
32	California	1,163
33	Wisconsin	1,155
34	Minnesota	1,136
35	Wyoming	1,117
36	Nebraska	1,087
37	Ohio	1,075
38	Colorado	1,021
39	Idaho	1,003
40	Oklahoma	997
41	North Carolina	985
42	Kentucky	979
43	New Hampshire	821
44	Virginia	815
45	Washington	617
46	Puerto Rico	595
47	Utah	553
48	Oregon	490
49	Maine	476
50	Alaska	367
51	Vermont	301
52	Hawaii	297

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Alabama	33
2	Arizona	29
3	Ohio	25
4	Delaware	18
5	Mississippi	13
6	Wyoming	13
7	California	12
8	Kansas	12
9	Nevada	12
10	Tennessee	12
11	Georgia	11
12	Missouri	11
13	New Jersey	11
14	Oklahoma	11
15	Rhode Island	11
16	Texas	11
17	Arkansas	10
18	Massachusetts	10
19	New Mexico	10
20	Pennsylvania	10
21	West Virginia	10
22	Iowa	9
23	North Carolina	9
24	South Carolina	9
25	District of Columbia	8
26	Florida	8
27	New York	8
28	Indiana	7
29	Kentucky	7
30	South Dakota	7
31	Washington	7
32	Connecticut	6
33	Louisiana	6
34	Wisconsin	6
35	Illinois	5
36	Maryland	5
37	Michigan	5
38	Virginia	5
39	Nebraska	4
40	Utah	3
41	Colorado	2
42	Idaho	2
43	Minnesota	2
44	Montana	2
45	New Hampshire	2
46	North Dakota	2
47	Vermont	2
48	Hawaii	1
49	Maine	1
50	Oregon	1
51	Puerto Rico	1
52	Alaska	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	311,005	1	99
Chattahoochee	Georgia	265,242	2	99
Bent	Colorado	249,238	3	99
Lincoln	Arkansas	238,867	4	99
Lake	Tennessee	236,032	5	99
Davidson	Tennessee	125,740	226	92
Richland	South Carolina	91,428	1284	59
York	South Carolina	85,088	1605	48
Orange	California	80,377	1832	41
Pierce	Washington	40,383	2901	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Buffalo	South Dakota	6,626	3	99
Dickey	North Dakota	6,568	4	99
Emporia city	Virginia	6,547	5	99
Davidson	Tennessee	1,158	1901	39
Richland	South Carolina	1,106	1966	37
Orange	California	1,100	1972	37
York	South Carolina	993	2123	32
Pierce	Washington	556	2681	14

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.